Open vs. Closed Claims: Unlocking Opportunities for Healthcare Analytics
In the complex healthcare analytics landscape, understanding the nuances of open and closed claims is critical for researchers and analysts. Open and closed claims each offer unique insights into the patient journey.
Open claims, sourced from various clearinghouses and warehouses across the U.S., present a vast collection of data, while closed claims, adjudicated by insurers, provide a detailed, individualized perspective. Open claims are rather like a wide-angle view of the forest, whereas closed claims are more like a view of the individual trees.
We caught up with Meg Richards, PhD, MPH and executive director of solutions at Panalgo, to discuss the strengths and limitations of open and closed claims in healthcare research.
What are administrative claims?
In healthcare, administrative claims are the bills submitted by physicians, hospitals, pharmacies, or other medical providers for office visits, hospital stays, other encounters, or sales of drugs and supplies.
Claims data refers to the information derived from the electronic processing of a healthcare claim. Whereas claims are handled primarily for the purpose of payment, the data obtained from these claims are also utilized for secondary healthcare research.
Some administrative claims are ‘open’ whereas others are ‘closed.’ What’s the difference?
Closed-payer claims data refers to information from payers that is provided directly by health insurance companies, or by a collection of employers sharing their employees’ health claims with consulting services, revealing (nearly) all of a patient’s healthcare activities within a fixed period of enrollment.
Open claims are captured through one or more of the following:
- Practice management systems, i.e., information systems that manage medical practices’ scheduling and billing
- ‘Switches’ or ‘clearinghouses’ that route claims from healthcare providers to insurers
- Pharmacy Benefit Managers that provide the link between pharmacies and insurance companies
Closed claims reveal a comprehensive view of a patient’s healthcare activities across geographies, but are limited to a specific time period and payer. Conversely, open claims provide a higher-level glimpse into the patient across multiple data sources and longer time frames, regardless of insurance provider. While open claims provide a broader perspective on the patient’s care history, they can be incomplete.
For many years, closed claims have been the first choice for health economists and epidemiologists profiling a patient cohort’s experience from disease onset to diagnosis and treatment. Open claims were used more for market uptake characterization and clinical trial recruitment. However, as more data become available and data quality enhancement methods mature, the distinguishing features and uses of open vs. closed claims can overlap.
What type of claims should you use and when?
The answer to this question greatly depends on the use case and scenario:
Is there a way to ‘engineer’ open claims data to make it more useful in characterizing the patient journey?
Absolutely. Open claims can be linked to closed claims to supply missing information. Duplicate patients can be identified and removed by matching patients with different IDs on gender, state, birth year, comorbidity score and other variables to drop all but one patient row. While this may sound easy, the task is actually quite nuanced, and the robustness of the result has much to do with the quality of the underlying data sources being linked.
What does the future look like for open and closed claims?
We’ll see more and more linkage of open and closed claims data with lab data, EMR data, and other real-world datasets featuring both structured and unstructured data. With more complete and connected datasets, pharma companies will be able to realize a comprehensive view of each and every patient.
Learn how MMIT and Panalgo’s Patient Access Analytics solution uses integrated coverage, claims, lab and pathways data to provide a unique view of payer and prescriber behavior.